Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 MiB
Average record size in memory168.0 B

Variable types

Numeric9
Categorical12

Reproduction

Analysis started2025-07-08 23:58:07.770310
Analysis finished2025-07-08 23:58:16.797370
Duration9.03 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Age
Real number (ℝ)

Distinct63
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.2954
Minimum18
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-08T20:58:16.833744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21
Q134
median49
Q365
95-th percentile77
Maximum80
Range62
Interquartile range (IQR)31

Descriptive statistics

Standard deviation18.167574
Coefficient of variation (CV)0.36854502
Kurtosis-1.1984467
Mean49.2954
Median Absolute Deviation (MAD)16
Skewness-0.0066570465
Sum492954
Variance330.06074
MonotonicityNot monotonic
2025-07-08T20:58:16.884592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49 202
 
2.0%
71 187
 
1.9%
64 185
 
1.8%
43 182
 
1.8%
34 182
 
1.8%
62 181
 
1.8%
72 180
 
1.8%
66 179
 
1.8%
76 176
 
1.8%
40 174
 
1.7%
Other values (53) 8172
81.7%
ValueCountFrequency (%)
18 149
1.5%
19 155
1.6%
20 154
1.5%
21 162
1.6%
22 142
1.4%
23 160
1.6%
24 132
1.3%
25 168
1.7%
26 147
1.5%
27 142
1.4%
ValueCountFrequency (%)
80 160
1.6%
79 168
1.7%
78 159
1.6%
77 169
1.7%
76 176
1.8%
75 165
1.7%
74 157
1.6%
73 152
1.5%
72 180
1.8%
71 187
1.9%

Gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
5022 
0
4978 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 5022
50.2%
0 4978
49.8%

Length

2025-07-08T20:58:16.929239image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T20:58:16.959651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5022
50.2%
0 4978
49.8%

Most occurring characters

ValueCountFrequency (%)
1 5022
50.2%
0 4978
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5022
50.2%
0 4978
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5022
50.2%
0 4978
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5022
50.2%
0 4978
49.8%

Blood Pressure
Real number (ℝ)

Distinct61
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean149.7582
Minimum120
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-08T20:58:16.993789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile123
Q1134
median150
Q3165
95-th percentile177
Maximum180
Range60
Interquartile range (IQR)31

Descriptive statistics

Standard deviation17.556268
Coefficient of variation (CV)0.11723076
Kurtosis-1.2084489
Mean149.7582
Median Absolute Deviation (MAD)15
Skewness0.01384184
Sum1497582
Variance308.22256
MonotonicityNot monotonic
2025-07-08T20:58:17.046186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
134 214
 
2.1%
167 195
 
1.9%
150 193
 
1.9%
171 182
 
1.8%
140 181
 
1.8%
142 181
 
1.8%
170 181
 
1.8%
133 178
 
1.8%
126 178
 
1.8%
129 177
 
1.8%
Other values (51) 8140
81.4%
ValueCountFrequency (%)
120 174
1.7%
121 162
1.6%
122 161
1.6%
123 161
1.6%
124 169
1.7%
125 148
1.5%
126 178
1.8%
127 169
1.7%
128 171
1.7%
129 177
1.8%
ValueCountFrequency (%)
180 148
1.5%
179 140
1.4%
178 155
1.6%
177 172
1.7%
176 143
1.4%
175 167
1.7%
174 149
1.5%
173 167
1.7%
172 165
1.7%
171 182
1.8%

Cholesterol Level
Real number (ℝ)

Distinct151
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225.4273
Minimum150
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-08T20:58:17.096357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile158
Q1187
median226
Q3263
95-th percentile293
Maximum300
Range150
Interquartile range (IQR)76

Descriptive statistics

Standard deviation43.510401
Coefficient of variation (CV)0.19301301
Kurtosis-1.1997294
Mean225.4273
Median Absolute Deviation (MAD)38
Skewness-0.0072496877
Sum2254273
Variance1893.155
MonotonicityNot monotonic
2025-07-08T20:58:17.145854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
226 96
 
1.0%
292 91
 
0.9%
186 84
 
0.8%
288 83
 
0.8%
185 83
 
0.8%
193 81
 
0.8%
255 79
 
0.8%
165 78
 
0.8%
162 78
 
0.8%
289 77
 
0.8%
Other values (141) 9170
91.7%
ValueCountFrequency (%)
150 61
0.6%
151 57
0.6%
152 72
0.7%
153 62
0.6%
154 65
0.7%
155 66
0.7%
156 62
0.6%
157 49
0.5%
158 61
0.6%
159 66
0.7%
ValueCountFrequency (%)
300 68
0.7%
299 71
0.7%
298 51
0.5%
297 60
0.6%
296 70
0.7%
295 62
0.6%
294 67
0.7%
293 66
0.7%
292 91
0.9%
291 55
0.5%

Exercise Habits
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2
3397 
1
3332 
0
3271 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row0
4th row2
5th row0

Common Values

ValueCountFrequency (%)
2 3397
34.0%
1 3332
33.3%
0 3271
32.7%

Length

2025-07-08T20:58:17.186713image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T20:58:17.209158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 3397
34.0%
1 3332
33.3%
0 3271
32.7%

Most occurring characters

ValueCountFrequency (%)
2 3397
34.0%
1 3332
33.3%
0 3271
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 3397
34.0%
1 3332
33.3%
0 3271
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 3397
34.0%
1 3332
33.3%
0 3271
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 3397
34.0%
1 3332
33.3%
0 3271
32.7%

Smoking
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
5148 
0
4852 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5148
51.5%
0 4852
48.5%

Length

2025-07-08T20:58:17.240067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T20:58:17.261848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5148
51.5%
0 4852
48.5%

Most occurring characters

ValueCountFrequency (%)
1 5148
51.5%
0 4852
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5148
51.5%
0 4852
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5148
51.5%
0 4852
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5148
51.5%
0 4852
48.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
5025 
1
4975 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 5025
50.2%
1 4975
49.8%

Length

2025-07-08T20:58:17.289223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T20:58:17.309545image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5025
50.2%
1 4975
49.8%

Most occurring characters

ValueCountFrequency (%)
0 5025
50.2%
1 4975
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5025
50.2%
1 4975
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5025
50.2%
1 4975
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5025
50.2%
1 4975
49.8%

Diabetes
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
5048 
1
4952 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 5048
50.5%
1 4952
49.5%

Length

2025-07-08T20:58:17.342258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T20:58:17.369862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5048
50.5%
1 4952
49.5%

Most occurring characters

ValueCountFrequency (%)
0 5048
50.5%
1 4952
49.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5048
50.5%
1 4952
49.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5048
50.5%
1 4952
49.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5048
50.5%
1 4952
49.5%

BMI
Real number (ℝ)

Distinct9979
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.077274
Minimum18.002837
Maximum39.996954
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-08T20:58:17.417898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum18.002837
5-th percentile19.145012
Q123.668887
median29.079492
Q334.509009
95-th percentile38.872393
Maximum39.996954
Range21.994117
Interquartile range (IQR)10.840121

Descriptive statistics

Standard deviation6.3001558
Coefficient of variation (CV)0.21666941
Kurtosis-1.1774461
Mean29.077274
Median Absolute Deviation (MAD)5.4192347
Skewness-0.02136825
Sum290772.74
Variance39.691964
MonotonicityNot monotonic
2025-07-08T20:58:17.474942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.07949159 22
 
0.2%
24.99159109 1
 
< 0.1%
18.05695757 1
 
< 0.1%
28.73666675 1
 
< 0.1%
27.84084544 1
 
< 0.1%
23.68829856 1
 
< 0.1%
27.81803869 1
 
< 0.1%
22.47766533 1
 
< 0.1%
33.62495301 1
 
< 0.1%
37.99327298 1
 
< 0.1%
Other values (9969) 9969
99.7%
ValueCountFrequency (%)
18.00283694 1
< 0.1%
18.0070582 1
< 0.1%
18.00870862 1
< 0.1%
18.00875696 1
< 0.1%
18.00891781 1
< 0.1%
18.01162775 1
< 0.1%
18.01450821 1
< 0.1%
18.0147327 1
< 0.1%
18.01572549 1
< 0.1%
18.01600423 1
< 0.1%
ValueCountFrequency (%)
39.9969538 1
< 0.1%
39.99579742 1
< 0.1%
39.99459813 1
< 0.1%
39.98949339 1
< 0.1%
39.98901483 1
< 0.1%
39.9888456 1
< 0.1%
39.97948634 1
< 0.1%
39.97591451 1
< 0.1%
39.97447577 1
< 0.1%
39.97063009 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
5048 
0
4952 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5048
50.5%
0 4952
49.5%

Length

2025-07-08T20:58:17.521127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T20:58:17.545614image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5048
50.5%
0 4952
49.5%

Most occurring characters

ValueCountFrequency (%)
1 5048
50.5%
0 4952
49.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5048
50.5%
0 4952
49.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5048
50.5%
0 4952
49.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5048
50.5%
0 4952
49.5%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
5025 
0
4975 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 5025
50.2%
0 4975
49.8%

Length

2025-07-08T20:58:17.575446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T20:58:17.597347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 5025
50.2%
0 4975
49.8%

Most occurring characters

ValueCountFrequency (%)
1 5025
50.2%
0 4975
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5025
50.2%
0 4975
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5025
50.2%
0 4975
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5025
50.2%
0 4975
49.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
5062 
1
4938 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 5062
50.6%
1 4938
49.4%

Length

2025-07-08T20:58:17.624537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T20:58:17.668320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 5062
50.6%
1 4938
49.4%

Most occurring characters

ValueCountFrequency (%)
0 5062
50.6%
1 4938
49.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5062
50.6%
1 4938
49.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5062
50.6%
1 4938
49.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5062
50.6%
1 4938
49.4%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
2
5086 
1
2488 
3
2426 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 5086
50.9%
1 2488
24.9%
3 2426
24.3%

Length

2025-07-08T20:58:17.698257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T20:58:17.730931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2 5086
50.9%
1 2488
24.9%
3 2426
24.3%

Most occurring characters

ValueCountFrequency (%)
2 5086
50.9%
1 2488
24.9%
3 2426
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5086
50.9%
1 2488
24.9%
3 2426
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5086
50.9%
1 2488
24.9%
3 2426
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5086
50.9%
1 2488
24.9%
3 2426
24.3%

Stress Level
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
1
3409 
0
3320 
2
3271 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 3409
34.1%
0 3320
33.2%
2 3271
32.7%

Length

2025-07-08T20:58:17.778604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T20:58:17.804382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 3409
34.1%
0 3320
33.2%
2 3271
32.7%

Most occurring characters

ValueCountFrequency (%)
1 3409
34.1%
0 3320
33.2%
2 3271
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3409
34.1%
0 3320
33.2%
2 3271
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3409
34.1%
0 3320
33.2%
2 3271
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3409
34.1%
0 3320
33.2%
2 3271
32.7%

Sleep Hours
Real number (ℝ)

Distinct9975
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9913593
Minimum4.0006055
Maximum9.9999523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-08T20:58:17.850233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum4.0006055
5-th percentile4.2787162
Q15.4552881
median7.0032523
Q38.5279381
95-th percentile9.7057177
Maximum9.9999523
Range5.9993468
Interquartile range (IQR)3.07265

Descriptive statistics

Standard deviation1.7510023
Coefficient of variation (CV)0.25045234
Kurtosis-1.2197527
Mean6.9913593
Median Absolute Deviation (MAD)1.5364638
Skewness0.00012065864
Sum69913.593
Variance3.0660089
MonotonicityNot monotonic
2025-07-08T20:58:17.899755image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.003252335 26
 
0.3%
7.63322838 1
 
< 0.1%
6.600080829 1
 
< 0.1%
8.017111756 1
 
< 0.1%
4.55997574 1
 
< 0.1%
9.600425292 1
 
< 0.1%
4.74476551 1
 
< 0.1%
8.002935673 1
 
< 0.1%
7.810732471 1
 
< 0.1%
8.188182738 1
 
< 0.1%
Other values (9965) 9965
99.7%
ValueCountFrequency (%)
4.000605496 1
< 0.1%
4.000772948 1
< 0.1%
4.001081817 1
< 0.1%
4.00211061 1
< 0.1%
4.002624344 1
< 0.1%
4.002661697 1
< 0.1%
4.002895496 1
< 0.1%
4.003256213 1
< 0.1%
4.005054241 1
< 0.1%
4.005261905 1
< 0.1%
ValueCountFrequency (%)
9.999952254 1
< 0.1%
9.99915082 1
< 0.1%
9.998776396 1
< 0.1%
9.998525334 1
< 0.1%
9.9982211 1
< 0.1%
9.998006734 1
< 0.1%
9.997739297 1
< 0.1%
9.997299691 1
< 0.1%
9.997255758 1
< 0.1%
9.996252129 1
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
3420 
2
3330 
1
3250 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row2
5th row2

Common Values

ValueCountFrequency (%)
0 3420
34.2%
2 3330
33.3%
1 3250
32.5%

Length

2025-07-08T20:58:17.953632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T20:58:17.987357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 3420
34.2%
2 3330
33.3%
1 3250
32.5%

Most occurring characters

ValueCountFrequency (%)
0 3420
34.2%
2 3330
33.3%
1 3250
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3420
34.2%
2 3330
33.3%
1 3250
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3420
34.2%
2 3330
33.3%
1 3250
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3420
34.2%
2 3330
33.3%
1 3250
32.5%

Triglyceride Level
Real number (ℝ)

Distinct301
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.7325
Minimum100
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-08T20:58:18.025243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile114
Q1176
median250
Q3326
95-th percentile387
Maximum400
Range300
Interquartile range (IQR)150

Descriptive statistics

Standard deviation86.953962
Coefficient of variation (CV)0.34679972
Kurtosis-1.1944603
Mean250.7325
Median Absolute Deviation (MAD)75
Skewness0.0062157577
Sum2507325
Variance7560.9914
MonotonicityNot monotonic
2025-07-08T20:58:18.073584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
250 64
 
0.6%
307 48
 
0.5%
215 47
 
0.5%
228 47
 
0.5%
173 46
 
0.5%
400 46
 
0.5%
349 46
 
0.5%
375 46
 
0.5%
186 45
 
0.4%
251 45
 
0.4%
Other values (291) 9520
95.2%
ValueCountFrequency (%)
100 38
0.4%
101 32
0.3%
102 31
0.3%
103 36
0.4%
104 24
0.2%
105 33
0.3%
106 31
0.3%
107 35
0.4%
108 25
0.2%
109 34
0.3%
ValueCountFrequency (%)
400 46
0.5%
399 35
0.4%
398 33
0.3%
397 41
0.4%
396 38
0.4%
395 30
0.3%
394 41
0.4%
393 39
0.4%
392 34
0.3%
391 25
0.2%

Fasting Blood Sugar
Real number (ℝ)

Distinct81
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.1419
Minimum80
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-08T20:58:18.119851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile84
Q199
median120
Q3141
95-th percentile156
Maximum160
Range80
Interquartile range (IQR)42

Descriptive statistics

Standard deviation23.558053
Coefficient of variation (CV)0.19608524
Kurtosis-1.2261418
Mean120.1419
Median Absolute Deviation (MAD)21
Skewness-0.0088846842
Sum1201419
Variance554.98186
MonotonicityNot monotonic
2025-07-08T20:58:18.169158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119 151
 
1.5%
149 149
 
1.5%
148 148
 
1.5%
96 147
 
1.5%
85 141
 
1.4%
153 139
 
1.4%
139 139
 
1.4%
120 139
 
1.4%
111 139
 
1.4%
141 138
 
1.4%
Other values (71) 8570
85.7%
ValueCountFrequency (%)
80 117
1.2%
81 127
1.3%
82 119
1.2%
83 134
1.3%
84 130
1.3%
85 141
1.4%
86 98
1.0%
87 127
1.3%
88 128
1.3%
89 118
1.2%
ValueCountFrequency (%)
160 126
1.3%
159 128
1.3%
158 124
1.2%
157 116
1.2%
156 137
1.4%
155 103
1.0%
154 135
1.4%
153 139
1.4%
152 113
1.1%
151 130
1.3%

CRP Level
Real number (ℝ)

Distinct9975
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4722005
Minimum0.0036467124
Maximum14.997087
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-08T20:58:18.223152image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0036467124
5-th percentile0.71517786
Q13.6818004
median7.4721644
Q311.244879
95-th percentile14.207879
Maximum14.997087
Range14.99344
Interquartile range (IQR)7.5630788

Descriptive statistics

Standard deviation4.334601
Coefficient of variation (CV)0.58009699
Kurtosis-1.2047256
Mean7.4722005
Median Absolute Deviation (MAD)3.7848548
Skewness-0.0040739525
Sum74722.005
Variance18.788766
MonotonicityNot monotonic
2025-07-08T20:58:18.270337image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.472164393 26
 
0.3%
12.96924569 1
 
< 0.1%
7.82322549 1
 
< 0.1%
1.761838151 1
 
< 0.1%
5.630721103 1
 
< 0.1%
10.1942412 1
 
< 0.1%
13.84827798 1
 
< 0.1%
12.90229278 1
 
< 0.1%
2.452627325 1
 
< 0.1%
0.9317634501 1
 
< 0.1%
Other values (9965) 9965
99.7%
ValueCountFrequency (%)
0.003646712362 1
< 0.1%
0.008811190431 1
< 0.1%
0.01058465733 1
< 0.1%
0.01133684843 1
< 0.1%
0.0126981196 1
< 0.1%
0.01487135133 1
< 0.1%
0.0154669267 1
< 0.1%
0.01928247725 1
< 0.1%
0.02003004144 1
< 0.1%
0.02054069269 1
< 0.1%
ValueCountFrequency (%)
14.99708673 1
< 0.1%
14.99662473 1
< 0.1%
14.9964313 1
< 0.1%
14.9949198 1
< 0.1%
14.98959621 1
< 0.1%
14.98177895 1
< 0.1%
14.98171819 1
< 0.1%
14.97812258 1
< 0.1%
14.97652019 1
< 0.1%
14.97579734 1
< 0.1%

Homocysteine Level
Real number (ℝ)

Distinct9981
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.456177
Minimum5.0002365
Maximum19.999037
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size78.2 KiB
2025-07-08T20:58:18.319179image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.0002365
5-th percentile5.7129076
Q18.7297713
median12.409395
Q316.130968
95-th percentile19.244496
Maximum19.999037
Range14.998801
Interquartile range (IQR)7.4011969

Descriptive statistics

Standard deviation4.3191003
Coefficient of variation (CV)0.34674365
Kurtosis-1.1761192
Mean12.456177
Median Absolute Deviation (MAD)3.6971984
Skewness0.0079593029
Sum124561.77
Variance18.654628
MonotonicityNot monotonic
2025-07-08T20:58:18.370011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.40939538 20
 
0.2%
12.3872504 1
 
< 0.1%
17.69504762 1
 
< 0.1%
15.83024389 1
 
< 0.1%
5.358912056 1
 
< 0.1%
9.445296887 1
 
< 0.1%
12.72935985 1
 
< 0.1%
8.062251698 1
 
< 0.1%
11.85308719 1
 
< 0.1%
15.25711017 1
 
< 0.1%
Other values (9971) 9971
99.7%
ValueCountFrequency (%)
5.000236488 1
< 0.1%
5.001208789 1
< 0.1%
5.002809604 1
< 0.1%
5.002877423 1
< 0.1%
5.006436734 1
< 0.1%
5.009292436 1
< 0.1%
5.010650326 1
< 0.1%
5.010787553 1
< 0.1%
5.011536896 1
< 0.1%
5.011873943 1
< 0.1%
ValueCountFrequency (%)
19.99903699 1
< 0.1%
19.99875896 1
< 0.1%
19.99703165 1
< 0.1%
19.99654662 1
< 0.1%
19.99606262 1
< 0.1%
19.99591638 1
< 0.1%
19.99329153 1
< 0.1%
19.9926266 1
< 0.1%
19.99202839 1
< 0.1%
19.99164212 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.2 KiB
0
8000 
1
2000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters10000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 8000
80.0%
1 2000
 
20.0%

Length

2025-07-08T20:58:18.413405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-08T20:58:18.434933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0 8000
80.0%
1 2000
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 8000
80.0%
1 2000
 
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 8000
80.0%
1 2000
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 8000
80.0%
1 2000
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 8000
80.0%
1 2000
 
20.0%

Interactions

2025-07-08T20:58:16.252626image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:08.950253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.381747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.778809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.146014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.479966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.829679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:15.192190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:15.570461image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:16.292777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:09.015869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.425090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.821553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.182256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.519642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.867367image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:15.233555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:15.609852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:16.331768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:09.057442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.490333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.863076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.218002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.558230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.905407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:15.274314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:15.650359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:16.371883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.119818image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.531945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.902487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.255564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.598793image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.944479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:15.316888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:15.689476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:16.410506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.161507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.574175image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.939904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.287320image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.637390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.980558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:15.355506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:15.726316image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:16.452994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.215032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.612929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:13.983026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-08T20:58:13.298529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-08T20:58:13.737967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:14.105645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-07-08T20:58:15.527066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-08T20:58:16.209515image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-08T20:58:18.473731image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeAlcohol ConsumptionBMIBlood PressureCRP LevelCholesterol LevelDiabetesExercise HabitsFamily Heart DiseaseFasting Blood SugarGenderHeart Disease StatusHigh Blood PressureHigh LDL CholesterolHomocysteine LevelLow HDL CholesterolSleep HoursSmokingStress LevelSugar ConsumptionTriglyceride Level
Age1.0000.0210.011-0.0210.0090.0110.0000.0000.009-0.0060.0000.0230.0370.035-0.0070.0140.0030.0000.0190.005-0.008
Alcohol Consumption0.0211.0000.0000.0000.0110.0120.0000.0040.0070.0000.0220.0110.0000.0260.0170.0000.0050.0000.0120.0240.000
BMI0.0110.0001.0000.005-0.0170.0220.0000.0000.0000.0060.0190.0160.0160.0220.0040.027-0.0010.0290.0130.0000.005
Blood Pressure-0.0210.0000.0051.000-0.010-0.0120.0110.0000.000-0.0120.0190.0240.0110.000-0.0030.0000.0010.0150.0280.0210.008
CRP Level0.0090.011-0.017-0.0101.000-0.0180.0000.0000.0110.0100.0000.0000.0000.000-0.0100.0000.0020.0160.0000.000-0.006
Cholesterol Level0.0110.0120.022-0.012-0.0181.0000.0170.0190.0160.0000.0000.0000.0200.000-0.0060.0000.0110.0000.0000.0110.001
Diabetes0.0000.0000.0000.0110.0000.0171.0000.0000.0000.0000.0000.0000.0000.0000.0210.0000.0160.0070.0230.0000.000
Exercise Habits0.0000.0040.0000.0000.0000.0190.0001.0000.0000.0000.0000.0000.0000.0150.0000.0000.0000.0170.0000.0000.010
Family Heart Disease0.0090.0070.0000.0000.0110.0160.0000.0001.0000.0000.0000.0000.0150.0050.0120.0000.0200.0000.0020.0000.000
Fasting Blood Sugar-0.0060.0000.006-0.0120.0100.0000.0000.0000.0001.0000.0000.0000.0200.020-0.0200.0000.0090.0000.0000.0000.008
Gender0.0000.0220.0190.0190.0000.0000.0000.0000.0000.0001.0000.0140.0100.0040.0330.0000.0250.0040.0040.0000.029
Heart Disease Status0.0230.0110.0160.0240.0000.0000.0000.0000.0000.0000.0141.0000.0000.0000.0000.0000.0000.0000.0230.0000.000
High Blood Pressure0.0370.0000.0160.0110.0000.0200.0000.0000.0150.0200.0100.0001.0000.0000.0000.0040.0160.0170.0000.0080.000
High LDL Cholesterol0.0350.0260.0220.0000.0000.0000.0000.0150.0050.0200.0040.0000.0001.0000.0110.0000.0080.0000.0150.0000.005
Homocysteine Level-0.0070.0170.004-0.003-0.010-0.0060.0210.0000.012-0.0200.0330.0000.0000.0111.0000.000-0.0200.0180.0000.010-0.006
Low HDL Cholesterol0.0140.0000.0270.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0001.0000.0000.0070.0000.0000.000
Sleep Hours0.0030.005-0.0010.0010.0020.0110.0160.0000.0200.0090.0250.0000.0160.008-0.0200.0001.0000.0170.0000.0000.002
Smoking0.0000.0000.0290.0150.0160.0000.0070.0170.0000.0000.0040.0000.0170.0000.0180.0070.0171.0000.0000.0000.000
Stress Level0.0190.0120.0130.0280.0000.0000.0230.0000.0020.0000.0040.0230.0000.0150.0000.0000.0000.0001.0000.0090.000
Sugar Consumption0.0050.0240.0000.0210.0000.0110.0000.0000.0000.0000.0000.0000.0080.0000.0100.0000.0000.0000.0091.0000.013
Triglyceride Level-0.0080.0000.0050.008-0.0060.0010.0000.0100.0000.0080.0290.0000.0000.005-0.0060.0000.0020.0000.0000.0131.000

Missing values

2025-07-08T20:58:16.655430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-08T20:58:16.734357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

AgeGenderBlood PressureCholesterol LevelExercise HabitsSmokingFamily Heart DiseaseDiabetesBMIHigh Blood PressureLow HDL CholesterolHigh LDL CholesterolAlcohol ConsumptionStress LevelSleep HoursSugar ConsumptionTriglyceride LevelFasting Blood SugarCRP LevelHomocysteine LevelHeart Disease Status
056.01153.0155.0211024.991591110317.6332281342.0120.012.96924612.3872500
169.00146.0286.0201125.221799010228.7440341133.0157.09.35538919.2988750
246.01126.0216.0000029.855447011104.4404400393.092.012.70987311.2309260
332.00122.0293.0211024.130477101125.2494052293.094.012.5090465.9619580
460.01166.0242.0011120.486289100127.0309712263.0154.010.3812598.1538870
525.01152.0257.0010028.144681000115.5048760126.091.04.29757510.8159830
678.00121.0175.0211118.042332010219.2409111107.085.011.58298319.6594610
738.00161.0187.0011134.736683000117.8410082228.0111.04.92938117.1465990
856.00135.0291.0001134.493112111306.9414032317.0103.05.1190156.0511290
975.01144.0252.0011030.142149001114.0026622199.096.010.0056987.6043570
AgeGenderBlood PressureCholesterol LevelExercise HabitsSmokingFamily Heart DiseaseDiabetesBMIHigh Blood PressureLow HDL CholesterolHigh LDL CholesterolAlcohol ConsumptionStress LevelSleep HoursSugar ConsumptionTriglyceride LevelFasting Blood SugarCRP LevelHomocysteine LevelHeart Disease Status
999055.00131.0160.0210132.363462000115.0187831267.0129.00.60321311.2982521
999141.00160.0241.0101139.346342111224.3436660178.0106.04.71886414.2197911
999268.00169.0291.0110022.839718010206.0575092299.0142.03.32102011.9102441
999327.00153.0188.0100128.173059110214.8348422300.0118.06.21670013.1148081
999473.00144.0191.0111139.459620000207.5491141200.088.01.1549048.0217321
999525.00136.0243.0110018.788791101226.8349541343.0133.03.58881419.1320041
999638.01172.0154.0100031.856801101228.2477840377.083.02.6582679.7157091
999773.01152.0201.0210126.899911011204.4367620248.088.04.4088679.4924291
999823.01142.0299.0010134.964026101228.5263291113.0153.07.21563411.8734861
999938.00128.0193.0111125.111295011315.6593942121.0149.014.3878106.2085311